Modeling the Morphological Evolution of Cellulose Nanocrystals via Multi-Dimensional Population Balances

Cellulose nanocrystals (CNCs) are highly crystalline, rod-like nanomaterials with functional properties governed by their morphology, specifically their size and shape. However, achieving precise morphological control during synthesis remains challenging due to a limited understanding of the underlying breakage mechanisms. This thesis develops a continuum-based framework using multi-dimensional population balance equations to model the evolution of CNC morphological distributions. A novel data-driven identification algorithm is devised to infer multi-dimensional breakage kernels directly from data. Overall, this work advances the mechanistic understanding of CNC morphological evolution and provides a systematic pathway for tailoring CNC morphology.